Microsatellite variation and recombination rate in the human genome

Background (purifying) selection on deleterious mutations is expected to remove linked neutral mutations from a population, resulting in a positive correlation between recombination rate and levels of neutral genetic variation, even for markers with high mutation rates. We tested this prediction of the background selection model by comparing recombination rate and levels of microsatellite polymorphism in humans. Published data for 28 unrelated Europeans were used to estimate microsatellite polymorphism (number of alleles, heterozygosity, and variance in allele size) for loci throughout the genome. Recombination rates were estimated from comparisons of genetic and physical maps. First, we analyzed 61 loci from chromosome 22, using the complete sequence of this chromosome to provide exact physical locations. These 61 microsatellites showed no correlation between levels of variation and recombination rate. We then used radiation-hybrid and cytogenetic maps to calculate recombination rates throughout the genome. Recombination rates varied by more than one order of magnitude, and most chromosomes showed significant suppression of recombination near the centromere. Genome-wide analyses provided no evidence for a strong positive correlation between recombination rate and polymorphism, although analyses of loci with at least 20 repeats suggested a weak positive correlation. Comparisons of microsatellites in lowest-recombination and highest-recombination regions also revealed no difference in levels of polymorphism. Together, these results indicate that background selection is not a major determinant of microsatellite variation in humans.     

Genomic screening in family-based association testing

Due to the recent gains in the availability of single-nucleotide polymorphism data, genome-wide association testing has become feasible. It is hoped that this additional data may confirm the presence of disease susceptibility loci, and identify new genetic determinants of disease. However, the problem of multiple comparisons threatens to diminish any potential gains from this newly available data. To circumvent the multiple comparisons issue, we utilize a recently developed screening technique using family-based association testing. This screening methodology allows for the identification of the most promising single-nucleotide polymorphisms for testing without biasing the nominal significance level of our test statistic. We compare the results of our screening technique across univariate and multivariate family-based association tests. From our analyses, we observe that the screening technique, applied to different settings, is fairly consistent in identifying optimal markers for testing. One of the identified markers, TSC0047225, was significantly associated with both the ttth1 (p = 0.004) and ttth1-ttth4 (p = 0.004) phenotype(s). We find that both univariate- and multivariate-based screening techniques are powerful tools for detecting an association.     

Consensus features of microsatellite distribution: Microsatellite contents are universally correlated with recombination rates and are preferentially depressed by centromeres in multicellular eukaryotic genomes

Microsatellite DNA is highly polymorphic and informative, which makes its distribution pattern and its associations very valuable for marker applications and genomic research in evolution. Using computational and statistical approaches, based on database technology, we have demonstrated that microsatellite content is consistently and significantly 2 to 5 fold lower than the average chromosomal level in the centromeric and pericentromeric regions of the chromosomes of two plant species, Arabidopsis thaliana and Oryza sativa. We conducted a path coefficient analysis to compare the direct effect of microsatellites (from mono-nucleotide through to penta-nucleotide repeats) on recombination rates. The results revealed that tri- and penta-nucleotide microsatellites significantly influence recombination rates. In the human genome, tri-, tetra- and mono-nucleotide microsatellites, in decreasing order, make significant direct contributions to recombination rates, according to DECODE, GENTHON, and MARSHFIELD averages. Path coefficient analysis in rice and human genomes of the impact of di-nucleotide microsatellites of different motifs on recombination rates indicate that motifs with either A or T have an effect, resulting in increased recombination rates for microsatellites with motifs consisting of 50% A or T, such as AG, TC, CA, TG. Conversely, microsatellites with motifs consisting of only A & T or G & C, such as AT, TA, GC or CG, have decreased recombination rates. The extremely low microsatellite content in centromeric and pericentromeric regions, as well as the quantitative association of microsatellite sequences with the recombination rate at the genome level, suggests that purifying selection in genome evolution creates a balance between genomic polymorphisms and the biological function of sequences in a genome.     

Genomics and genome-wide association studies: an integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.     

A comparison in association and linkage genome-wide scans for alcoholism susceptibility genes using single-nucleotide polymorphisms

We conducted genome-wide linkage scans using both microsatellite and single-nucleotide polymorphism (SNP) markers. Regions showing the strongest evidence of linkage to alcoholism susceptibility genes were identified. Haplotype analyses using a sliding-window approach for SNPs in these regions were performed. In addition, we performed a genome-wide association scan using SNP data. SNPs in these regions with evidence of association (P 相似文献   

Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis

There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (approximately 2.9 cM compared to approximately 6.7 cM apart) provided slightly less information content (approximately 0.83 compared to approximately 0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan.     

A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method

Common human disorders, such as alcoholism, may be the result of interactions of many genes as well as environmental risk factors. Therefore, it is important to incorporate gene x gene and gene x environment interactions in complex disease gene mapping. In this study, we applied a robust Bayesian genome screening method that can incorporate interaction effects to map genes underlying alcoholism through its application to the data of the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. Our Bayesian genome screening method uses the regression-based stochastic variable selection, coupled with the new Haseman-Elston method to identify markers linked to phenotypes of interest. Compared to traditional linkage methods based on single-gene disease models, our method allows for multilocus disease models for simultaneous screening including both main and interaction (epistatic) effects. It is conceptually simple and computationally efficient through the use of Gibbs sampler. We conducted genome-wide analysis and comparison between scans based on microsatellites and single-nucleotide polymorphisms. A total of 328 microsatellites and 11,560 single-nucleotide polymorphisms (by Affymetrix) on 22 autosomal chromosomes and sex chromosome were used.     

Whole-genome linkage analysis in mapping alcoholism genes using single-nucleotide polymorphisms and microsatellites

There is currently a great interest in using single-nucleotide polymorphisms (SNPs) in genetic linkage and association studies because of the abundance of SNPs as well as the availability of high-throughput genotyping technologies. In this study, we compared the performance of whole-genome scans using SNPs with microsatellites on 143 pedigrees from the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were used in our analyses. We found that the results from the two scans had good overall concordance. One region on chromosome 2 and two regions on chromosome 7 showed significant linkage signals (i.e., NPL >or= 2) for alcoholism from both the SNP and microsatellite scans. The different results observed between the two scans may be explained by the difference observed in information content between the SNPs and the microsatellites.     

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association

For the identification of susceptibility loci in complex diseases the choice of the target phenotype is very important. We compared results of genome-wide searches for linkage or for association related to three phenotypes for alcohol use disorder. These are a behavioral score BQ, based on a 12-item questionnaire about drinking behavior and the subject's report of drinking-related health problems, and ERP pattern and ERP magnitude, both derived from the eyes closed resting ERP measures to quantify brain activity. Overall, we were able to identify 11 candidate regions for linkage. Only two regions were found to be related to both BQ and one of the ERP phenotypes. The genome-wide search for association using single-nucleotide polymorphisms did not yield interesting leads.     

Growth period QTL-allele constitution of global soybeans and its differential evolution changes in geographic adaptation versus maturity group extension

Soybean (Glycine max (L.) Merr.) has been disseminated globally as a photoperiod/temperature-sensitive crop with extremely diverse days to flowering (DTF) and days to maturity (DTM) values. A population with 371 global varieties covering 13 geographic regions and 13 maturity groups (MGs) was analyzed for its DTF and DTM QTL-allele constitution using restricted two-stage multi-locus genome-wide association study (RTM-GWAS). Genotypes with 20 701 genome-wide SNPLDBs (single-nucleotide polymorphism linkage disequilibrium blocks) containing 55 404 haplotypes were observed, and 52 DTF QTLs and 59 DTM QTLs (including 29 and 21 new ones) with 241 and 246 alleles (two to 13 per locus) were detected, explaining 84.8% and 74.4% of the phenotypic variance, respectively. The QTL-allele matrix characterized with all QTL-allele information of each variety in the global population was established and subsequently separated into geographic and MG set submatrices. Direct comparisons among them revealed that the genetic adaptation from the origin to geographic subpopulations was characterized by new allele/new locus emergence (mutation) but little allele exclusion (selection), while that from the primary MG set to emerged early and late MG sets was characterized by allele exclusion without allele emergence. The evolutionary changes involved mainly 72 DTF and 71 DTM alleles on 28 respective loci, 10–12 loci each with three to six alleles being most active. Further recombination potential for faster maturation (12–21 days) or slower maturation (14–56 days) supported allele convergence (recombination) as a constant genetic factor in addition to migration (inheritance). From the QTLs, 44 DTF and 36 DTM candidate genes were annotated and grouped respectively into nine biological processes, indicating multi-functional DTF/DTM genes are involved in a complex gene network. In summary, we identified QTL-alleles relatively thoroughly using RTM-GWAS for direct matrix comparisons and subsequent analysis.     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.     

Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism

The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0-2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.     

SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap

SUMMARY: The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies). AVAILABILITY: SNAP server is available at http://www.broad.mit.edu/mpg/snap/.     

Recombination locations and rates in beef cattle assessed from parent-offspring pairs

Background

Recombination events tend to occur in hotspots and vary in number among individuals. The presence of recombination influences the accuracy of haplotype phasing and the imputation of missing genotypes. Genes that influence genome-wide recombination rate have been discovered in mammals, yeast, and plants. Our aim was to investigate the influence of recombination on haplotype phasing, locate recombination hotspots, scan the genome for Quantitative Trait Loci (QTL) and identify candidate genes that influence recombination, and quantify the impact of recombination on the accuracy of genotype imputation in beef cattle.

Methods

2775 Angus and 1485 Limousin parent-verified sire/offspring pairs were genotyped with the Illumina BovineSNP50 chip. Haplotype phasing was performed with DAGPHASE and BEAGLE using UMD3.1 assembly SNP (single nucleotide polymorphism) coordinates. Recombination events were detected by comparing the two reconstructed chromosomal haplotypes inherited by each offspring with those of their sires. Expected crossover probabilities were estimated assuming no interference and a binomial distribution for the frequency of crossovers. The BayesB approach for genome-wide association analysis implemented in the GenSel software was used to identify genomic regions harboring QTL with large effects on recombination. BEAGLE was used to impute Angus genotypes from a 7K subset to the 50K chip.

Results

DAGPHASE was superior to BEAGLE in haplotype phasing, which indicates that linkage information from relatives can improve its accuracy. The estimated genetic length of the 29 bovine autosomes was 3097 cM, with a genome-wide recombination distance averaging 1.23 cM/Mb. 427 and 348 windows containing recombination hotspots were detected in Angus and Limousin, respectively, of which 166 were in common. Several significant SNPs and candidate genes, which influence genome-wide recombination were localized in QTL regions detected in the two breeds. High-recombination rates hinder the accuracy of haplotype phasing and genotype imputation.

Conclusions

Small population sizes, inadequate half-sib family sizes, recombination, gene conversion, genotyping errors, and map errors reduce the accuracy of haplotype phasing and genotype imputation. Candidate regions associated with recombination were identified in both breeds. Recombination analysis may improve the accuracy of haplotype phasing and genotype imputation from low- to high-density SNP panels.     

Heterogeneity in Rates of Recombination across the Mouse Genome

If loci are randomly distributed on a physical map, the density of markers on a genetic map will be inversely proportional to recombination rate. First proposed by MARY LYON, we have used this idea to estimate recombination rates from the Drosophila melanogaster linkage map. These results were compared with results of two other studies that estimated regional recombination rates in D. melanogaster using both physical and genetic maps. The three methods were largely concordant in identifying large-scale genomic patterns of recombination. The marker density method was then applied to the Mus musculus microsatellite linkage map. The distribution of microsatellites provided evidence for heterogeneity in recombination rates. Centromeric regions for several mouse chromosomes had significantly greater numbers of markers than expected, suggesting that recombination rates were lower in these regions. In contrast, most telomeric regions contained significantly fewer markers than expected. This indicates that recombination rates are elevated at the telomeres of many mouse chromosomes and is consistent with a comparison of the genetic and cytogenetic maps in these regions. The density of markers on a genetic map may provide a generally useful way to estimate regional recombination rates in species for which genetic, but not physical, maps are available.     

A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking

Genetic mechanisms underlying alcoholism are complex. Understanding the etiology of alcohol dependence and its comorbid conditions such as smoking is important because of the significant health concerns. In this report, we describe a method based on classification trees and deterministic forests for association studies to perform a genome-wide joint association analysis of alcoholism and smoking. This approach is used to analyze the single-nucleotide polymorphism data from the Collaborative Study on the Genetics of Alcoholism in the Genetic Analysis Workshop 14. Our analysis reaffirmed the importance of sex difference in alcoholism. Our analysis also identified genes that were reported in other studies of alcoholism and identified new genes or single-nucleotide polymorphisms that can be useful candidates for future studies.     

Bayesian Inference of Shared Recombination Hotspots Between Humans and Chimpanzees

Recombination generates variation and facilitates evolution. Recombination (or lack thereof) also contributes to human genetic disease. Methods for mapping genes influencing complex genetic diseases via association rely on linkage disequilibrium (LD) in human populations, which is influenced by rates of recombination across the genome. Comparative population genomic analyses of recombination using related primate species can identify factors influencing rates of recombination in humans. Such studies can indicate how variable hotspots for recombination may be both among individuals (or populations) and over evolutionary timescales. Previous studies have suggested that locations of recombination hotspots are not conserved between humans and chimpanzees. We made use of the data sets from recent resequencing projects and applied a Bayesian method for identifying hotspots and estimating recombination rates. We also reanalyzed SNP data sets for regions with known hotspots in humans using samples from the human and chimpanzee. The Bayes factors (BF) of shared recombination hotspots between human and chimpanzee across regions were obtained. Based on the analysis of the aligned regions of human chromosome 21, locations where the two species show evidence of shared recombination hotspots (with high BFs) were identified. Interestingly, previous comparative studies of human and chimpanzee that focused on the known human recombination hotspots within the β-globin and HLA regions did not find overlapping of hotspots. Our results show high BFs of shared hotspots at locations within both regions, and the estimated locations of shared hotspots overlap with the locations of human recombination hotspots obtained from sperm-typing studies.     

High-resolution recombination patterns in a region of human chromosome 21 measured by sperm typing

For decades, classical crossover studies and linkage disequilibrium (LD) analysis of genomic regions suggested that human meiotic crossovers may not be randomly distributed along chromosomes but are focused instead in "hot spots." Recent sperm typing studies provided data at very high resolution and accuracy that defined the physical limits of a number of hot spots. The data were also used to test whether patterns of LD can predict hot spot locations. These sperm typing studies focused on several small regions of the genome already known or suspected of containing a hot spot based on the presence of LD breakdown or previous experimental evidence of hot spot activity. Comparable data on target regions not specifically chosen using these two criteria is lacking but is needed to make an unbiased test of whether LD data alone can accurately predict active hot spots. We used sperm typing to estimate recombination in 17 almost contiguous ~5 kb intervals spanning 103 kb of human Chromosome 21. We found two intervals that contained new hot spots. The comparison of our data with recombination rates predicted by statistical analyses of LD showed that, overall, the two datasets corresponded well, except for one predicted hot spot that showed little crossing over. This study doubles the experimental data on recombination in men at the highest resolution and accuracy and supports the emerging genome-wide picture that recombination is localized in small regions separated by cold areas. Detailed study of one of the new hot spots revealed a sperm donor with a decrease in recombination intensity at the canonical recombination site but an increase in crossover activity nearby. This unique finding suggests that the position and intensity of hot spots may evolve by means of a concerted mechanism that maintains the overall recombination intensity in the region.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.